Abstract
Urban night vitality is a manifestation of a city's diverse life and economic prosperity. However, few existing studies pay attention to urban night vitality. Furthermore, large spatial scale research of urban night vitality remains scarce. To fill these gaps, this empirical study on the urban night vitality of central Shanghai is based on fine-grained mobile phone signaling data and other multisource data. The objective of this study is twofold. First, mobile phone signaling data (with refined spatiotemporal resolutions) is applied to measure urban night vitality on a city-level spatial scale. Second, the spatial lag model is utilized to identify factors that influence urban night vitality. The results indicate that urban vitality presents a stronger commercially driven spatial agglomeration pattern during the night, and the urban night vitality of young people has a more concentrated spatial pattern than that of middle-aged and older people. Furthermore, the spatial agglomeration pattern of urban night vitality diminishes as time passes. The results of the spatial lag model reveal that night businesses and mixed land use are significantly and positively related to urban night vitality. Specifically, bars and consumption levels of stores have the highest relative significance, followed by mixed land use. These findings illuminate the understanding of the spatiotemporal characteristics of urban night vitality, which has universal significance.
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Funding
This study was supported by the National Key R&D Program of China (2018YFB1601301), the Projects of International Cooperation and Exchanges from the National Natural Science Foundation of China (No. 72061137071), the Natural Science Foundation of Shanghai (No. 21ZR1466600), and the Central University Baasic Scientific Research Business Expenses Special Funds (No. 2022-5-YB-03).
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Appendix
Appendix
To select a model and make the necessary comparisons, the principal component scores of the main impact indicators and the UNV values were standardized, and then regression analysis was performed to obtain the results of the regression equation and the relationship between the strength of the four principal components. The OLS models for the 15 dependent variables each demonstrated a significant linear relationship (p < 0.001), and the fit was good (\({R}^{2}\) scores were all greater than 0.5). However, utilizing Moran’s I to calculate the residuals of the 15 OLS models revealed that all of them featured significant spatial autocorrelation, and the spatial regression model needed to be utilized. The results of the LM-test and the robust LM-test indicated that the LM-Lag, LM-Error, and robust LM-Lag of the 15 models passed the significance test at the 1% level, but the robust LM-Error failed. Therefore, the SLM was utilized for further regression estimation. The spatial regression results suggested that the AIC and SC of the 15 SLMs were significantly smaller than the corresponding OLS models, and both R2 and log-likelihood values were significantly larger than the corresponding OLS models. Utilizing the SLM to perform regression analysis on factors related to UNV was more scientific and reasonable than utilizing the OLS model. The relevant data appears in Tables 12 and 13.
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Liu, Z., Zhang, J., Luo, X. et al. Urban Night Vitality Measurements and Related Factors Based on Multisource Data: a Case Study of Central Shanghai. Appl. Spatial Analysis 17, 269–300 (2024). https://doi.org/10.1007/s12061-023-09540-z
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DOI: https://doi.org/10.1007/s12061-023-09540-z